Many IT organizations do not realize how they can use rich data sets that they already have for better operational and business decisions. The objective of an Open IT Operations Analytics (ITOA) architecture is to enable the discovery of new valuable relationships and insights derived from these combined data sets.

An Open ITOA architecture will drive improved IT operations, add business value, prevent vendor or data lock-in to proprietary systems, and provide a roadmap for the cost-effective expansion of the analytics architecture.

With the aim of providing practical and prescriptive guidance, this paper introduces a taxonomy that covers the primary data sets that serve as the foundation of Open ITOA.

Learn about machine data, wire data, agent data, and synthetic data, and each data source's strengths, limitations, and potential uses in an organization.

Gain control of your ITOA platform by avoiding vendor lock-in and minimizing switching costs if you decide to replace any particular data source technology.

Understand the benefits of an open, non-proprietary data store that enables contextual search and ad hoc queries, such as Elasticsearch or MongoDB.

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